Artificial Intelligence & ML

A scoped AI project at Neuros typically runs 8–16 weeks: two weeks of data discovery and evaluation-set design, four to eight weeks of model development, two to four weeks of shadow-mode validation and two weeks of rollout. What stretches the timeline is almost never the model — it is data access and approvals, which is why the data contract is signed in week one.

It depends on your data and your latency budget. Neuros starts with RAG when domain knowledge lives in documents, because RAG updates in a day while fine-tuning takes days. Fine-tuning wins when you have tens of thousands of labelled examples and a rigid output format. For classification and forecasting, gradient boosting is still cheaper, faster and more explainable than any language model.

Neuros builds an evaluation harness for every generative system: each response is scored for groundedness against its source, factual accuracy and format compliance. The scores run automatically on every deployment, and a build that falls below threshold cannot ship. The OWASP Top 10 for Large Language Model Applications is embedded in the same harness as test cases.

No. The Neuros default is that customer data stays inside that customer's own system, and training use is switched off in provider contracts. Where confidentiality demands it, the model runs on your premises or in your own cloud account. When personal data is involved, the processing inventory, retention periods and masking rules are defined as part of the architecture under Turkish law 6698 (KVKK) and the GDPR.

If you place a system on the EU market, yes. Regulation (EU) 2024/1689 phases in obligations by risk class; high-risk systems require risk management, data governance, technical documentation, record-keeping and human oversight. Neuros does not bolt these on afterwards — the classification is done in week one and the architecture follows from it.